trw.train.grad_cam

Module Contents

Classes

GradCam

Gradient-weighted Class Activation Mapping

Attributes

logger

trw.train.grad_cam.logger
class trw.train.grad_cam.GradCam(model: torch.nn.Module, find_convolution: Callable[[torch.nn.Module, Union[trw.basic_typing.Batch, torch.Tensor]], Optional[Mapping]] = graph_reflection.find_last_forward_convolution, post_process_output: Callable[[Any], torch.Tensor] = guided_back_propagation.post_process_output_id)

Gradient-weighted Class Activation Mapping

This is based on the paper “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization”, Ramprasaath R et al.

__call__(self, inputs: Union[trw.basic_typing.Batch, torch.Tensor], target_class_name: str = None, target_class: int = None) → Optional[Tuple[str, Mapping]]
Parameters

  • inputs – the inputs to be fed to the model

  • target_class_name –

    the output node to be used. If None: * if model output is a single tensor then use this as target output

    • else it will use the first OutputClassification output

  • target_class – the index of the class to explain the decision. If None, the class output will be used

Returns

a tuple (output name, a dictionary (input_name, GradCAMs))